Vector processors frequently suffer from inefficient memory accesses, particularly for strided and segment memory access patterns. While coalescing strided accesses is a natural solution, effectively gathering or scattering elements at fixed strides remains a significant challenge. Naive approaches typically rely on high-overhead crossbars that remap any byte in memory or registers to any position in registers or memory, leading to physical design issues. Meanwhile, segment operations require row-column transpositions, which are often handled using either element-level in-place transposition (degrading performance) or large buffer-based bulk transposition (incurring high area overhead). Both options are undesirable, highlighting a need for more efficient solutions. In this paper, we present EARTH, a novel vector memory access architecture designed to overcome these challenges through shifting-based optimizations. For strided accesses, EARTH integrates specialized shift networks for gathering and scattering strided elements. After coalescing multiple accesses into one request within the same cache line, data can be routed between memory and registers through the shifting network with minimal overhead. For segment operations, EARTH employs a shifted register bank that enables direct column-wise access, eliminating the need for dedicated segment buffers while providing highperformance, in-place bulk transposition at acceptable overhead. We implemented the entire EARTH design on FPGA with Chisel HDL based on an open-source RISC-V vector unit Saturn. Our evaluation demonstrates that EARTH enhances performance for strided memory accesses proportionally to their prevalence in workloads, achieving $\mathbf{4 x}-\mathbf{8 x}$ speedups in benchmarks dominated by strided operations. The architecture also delivers area-efficient segment handling. Compared to conventional designs, EARTH reducing hardware area by 9% and power consumption by 41%. By optimizing these necessary memory access patterns, EARTH significantly advances both the performance and efficiency of vector processors.

Cable transmission enables motors of robotic arm to operate lightweight and low-inertia joints remotely in various environments, but it also creates issues with motion coupling and cable routing that can reduce arm's control precision and performance. In this paper, we present a novel motion decoupling mechanism with low-friction to align the cables and efficiently transmit the motor's power. By arranging these mechanisms at the joints, we fabricate a fully decoupled and lightweight cable-driven robotic arm called D3-Arm with all the electrical components be placed at the base. Its 776 mm length moving part boasts six degrees of freedom (DOF) and only 1.6 kg weights. To address the issue of cable slack, a cable-pretension mechanism is integrated to enhance the stability of long-distance cable transmission. Through a series of comprehensive tests, D3-Arm demonstrated 1.29 mm average positioning error and 2.0 kg payload capacity, proving the practicality of the proposed decoupling mechanisms in cable-driven robotic arm.

Flexible cable-driven robotic arms (FCRAs) offer dexterous and compliant motion. Still, the inherent properties of cables, such as resilience, hysteresis, and friction, often lead to particular difficulties in modeling and control. This paper proposes a model predictive control (MPC) method that relies exclusively on input-output data, without a physical model, to improve the control accuracy of FCRAs. First, we develop an implicit model based on input-output data and integrate it into an MPC optimization framework. Second, a data selection algorithm (DSA) is introduced to filter the data that best characterize the system, thereby reducing the solution time per step to approximately 4 ms, which is an improvement of nearly 80%. Lastly, the influence of hyperparameters on tracking error is investigated through simulation. The proposed method has been validated on a real FCRA platform, including five-point positioning accuracy tests, a five-point response tracking test, and trajectory tracking for letter drawing. The results demonstrate that the average positioning accuracy is approximately 2.070 mm. Moreover, compared to the PID method with an average tracking error of 1.418°, the proposed method achieves an average tracking error of 0.541°.